Drone-Based Road Marking Condition Mapping: A Drone Imaging and Geospatial Pipeline for Asset Management

초록

What are the main findings? The method operates on corridor unmanned aerial vehicle (UAV) images collected at 100 m (Ground Sampling Distance 1.26 cm, 80/75%—overlap/sidelap), enabling reliable instance-level analysis across scenes. An end-to-end drone pipeline detects and segments individual road markings (You Only Look Once (YOLOv9e) + VGG16-U-Net) and estimates per-object Damage Rate (DR) via Kernel Density Estimation/Gaussian Mixture Model (KDE/GMM), with all outputs georeferenced to map coordinates. What are the implications of the main findings? The system achieves detector F1 93.642%/mAP50–95 65.553% and segmentation (unseen) mean intersection of union (mIoU) 94.21%/F1 97.00%, supporting network-scale inspection from drones. Map-ready records (Identification, location, Damage Rate) facilitate maintenance prioritization and integration with road asset inventories, reducing field time and traffic disruption compared with ground surveys. Highlights: This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by a UAV. A DJI Matrice 300 RTK (Real-Time Kinematic) equipped with a Zenmuse P1 camera (DJI, China) is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment. From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning. © 2026 by the authors.

키워드